Background: Manufacturers of heavy industrial equipment (wind mills, ocean vessels and equipment, offshore platforms, mining equipment) have to ensure uptime and hence productivity for their customers. This results in keeping inventories of the critical items. Such inventory carrying costs are huge. Still, companies face stock-outs for some parts due to unpredictability in failures while some parts which are available are never or rarely used. As the manufacturers move to servitized businesses promising the productivity and service guarantees for customers, they are in the need to better understand the usage profile and condition of the parts. Such understanding will help in optimizing equipment settings, in failure prediction of parts and in optimizing maintenance scheduling, in ensuring better spare parts planning, in designing effective contracts with customers through servitization and in enhancing the sustainability and performance across the supply chain.

Advancements in sensor technologies and telematics have made it possible to collect data about equipment conditions and usage on a real-time basis. There is a need on how to use such data and to develop novel approaches to use data from multiple sources for predicting failure of parts and for spare parts planning. Advancements in additive manufacturing technologies is also encouraging companies to adopt the technology for printing parts on demand and avoid carrying inventories. Multiple companies like Vestas, FL Smidth, Man Diesel and Turbo, Dong Energy, Alfa Laval, Wartsila, PJ Diesel, Kone Elevators and others will find digitization in spare parts planning to be relevant and have either started taking some steps or contemplating to invest in it.